Question and answer-based encyclopedia information analysis and product matching system

Abstract

The invention relates to the technical field of artificial intelligence and electronic commerce, and particularly discloses an encyclopedia information analysis and product matching system based on questions and answers. The system comprises a question and answer interface module, an encyclopedia information analysis module, a product matching module and an output module. The question and answer interface module is responsible for receiving and analyzing natural language questions of a user; the encyclopedia information analysis module extracts key knowledge points from the multi-source encyclopedia data; the product matching module generates a product combination scheme based on knowledge point matching; and the output module presents the result in a structured form. According to the system, accurate knowledge point extraction is realized through semantic vector calculation, a multi-dimensional evaluation system is adopted to generate a matching scheme, and personalized services are provided in combination with user portraits. According to the method, the knowledge query and the product recommendation are organically combined, the information processing efficiency and the recommendation accuracy are improved, and stable operation in a high-concurrency scene is supported. The system can be widely applied to the fields of electronic commerce, intelligent customer service and the like, and has important practical value.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and e-commerce technology, and more specifically, to a question-and-answer-based encyclopedia information analysis and product matching system. Background Technology

[0002] In recent years, with the rapid development of internet technology, this field has mainly gone through three stages of development both domestically and internationally: early-stage keyword-matching-based retrieval systems, mid-stage collaborative filtering-based recommendation algorithms, and the current personalized recommendation systems based on deep learning. The development trend indicates that the system is evolving towards multi-source information fusion, deep semantic understanding, and end-to-end intelligent decision-making. Currently, the research focus in this field has shifted from single product recommendations to providing comprehensive knowledge-guided solutions.

[0003] Existing technologies suffer from the following shortcomings: First, traditional question-answering systems and product recommendation systems operate independently, leading to a fragmented information processing flow. Users must complete operations on two separate platforms—one for knowledge querying and the other for product selection—resulting in inefficiency and a disjointed user experience. Second, current encyclopedic information analysis largely remains at the level of superficial semantic matching, lacking in-depth mining and structured processing of knowledge points, making it difficult to accurately understand the true intent and potential needs behind user queries. Third, current product pairing methods are mostly based on simple rules or statistical associations, failing to effectively utilize domain knowledge to guide product combination generation, resulting in recommendation results lacking logic and professionalism. Furthermore, existing systems generally lack a dynamic balancing mechanism for long-term user preferences and real-time needs, making them unable to adapt to complex and ever-changing real-world application scenarios. Finally, most systems employ a monolithic architecture design, exhibiting poor scalability and response latency issues when facing massive amounts of knowledge data and concurrent user requests.

[0004] Therefore, this paper proposes a question-and-answer-based encyclopedia information analysis and product matching system to address the following technical issues: how to achieve seamless integration of encyclopedia knowledge query and product recommendation; how to accurately extract and understand key knowledge points from multi-source heterogeneous data; how to generate logical product matching schemes based on deep semantic analysis; how to balance users' immediate needs and long-term preferences; and how to ensure the stability and response speed of the system under high-concurrency scenarios. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a question-and-answer based encyclopedia information analysis and product matching system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a question-and-answer-based encyclopedia information analysis and product matching system, the system comprising a question-and-answer interface module, an encyclopedia information analysis module, a product matching module, and an output module; the question-and-answer interface module is configured to receive natural language questions input by users and parse the question intent using natural language processing technology to generate a structured query; the encyclopedia information analysis module is configured to extract key knowledge points from multiple encyclopedia data sources based on the parsed question intent, the key knowledge points including entity attributes, entity relationships, and domain concepts; the product matching module is configured to, based on the key knowledge points, calculate the matching degree between product feature vectors and knowledge point vectors, filter relevant products from a product database, and generate product combination schemes containing matching logic; the output module is configured to present the product combination schemes to users in a structured form; wherein, the system achieves end-to-end processing from user questions to product matching schemes through the collaborative operation of each module, and the data flow in the processing flow is continuous and seamlessly connected.

[0007] Preferably, the knowledge point extraction process performed by the encyclopedia information analysis module includes: performing entity recognition and relation extraction on the original text obtained from multiple encyclopedia data sources to construct knowledge graph fragments; evaluating the importance of the knowledge graph fragments based on the question intent, and selecting key knowledge points whose relevance to the user intent exceeds a preset threshold; the importance evaluation is achieved by calculating the semantic relevance score between the knowledge points and the question intent, and the semantic relevance score is calculated using cosine similarity in the vector space model, where both the knowledge points and the question intent are represented as high-dimensional vectors.

[0008] Preferably, the semantic relevance score is calculated using the following formula: in, The knowledge point vector is obtained by embedding entities and relationships in a knowledge graph fragment; The question intent vector is obtained through semantic parsing of the user's question; when Value exceeds preset threshold ,in If the value ranges from 0.7 to 0.9, then the knowledge point is identified as a key knowledge point and passed to the product matching module.

[0009] Preferably, the product matching process performed by the product matching module includes: mapping key knowledge points to query vectors in the product feature space; calculating the matching degree between the query vectors and each product feature vector in the product database, wherein the matching degree is calculated by vector dot product or cosine similarity; and filtering products whose matching degree exceeds a second preset threshold. The products form a candidate product set, among which The value ranges from 0.6 to 0.8, and the matching degree is calculated based on the alignment between product attributes and key knowledge points.

[0010] Preferably, the product pairing module is further configured to: identify compatible product combinations in the candidate product set based on entity relationships and attribute constraints in key knowledge points; sort and optimize product combinations by evaluating compatibility scores and complementarity scores between products; the compatibility score is calculated based on the degree of matching of product technical specifications, the complementarity score is calculated based on the completeness of product functional coverage, and both the compatibility score and the complementarity score are converted into values ​​in the range of 0 to 1 through normalization processing.

[0011] Preferably, the final evaluation score of the product portfolio is calculated using the following formula: in, Indicates the compatibility score. Indicates complementarity score, Indicates based on user preferences and product features Personalized matching degree , and Let be the weight coefficient, and satisfy... , The value range is from 0.3 to 0.5. The value range is from 0.3 to 0.5. The value ranges from 0.2 to 0.4; the final evaluation score is used to prioritize the product mix and generate the best combination.

[0012] Preferably, the system further includes a user profiling module, configured to dynamically build and update a user preference model; the user preference model is established by analyzing users' historical interaction data, including the user's preference weight for product attributes and interest in product types; when generating product combination schemes, the product matching module uses the user preference model as input parameters to adjust the priority and filtering conditions of product matching.

[0013] Preferably, the user preference model influences the product matching process in the following way: a personalized matching score is obtained by performing a dot product operation on the user preference vector and the product feature vector; the personalized matching score is then weighted and fused with the matching degree based on key knowledge points to form the final product ranking; the weighted fusion is achieved through the following formula: in, This indicates the final match score. Indicates the personalized matching score. Indicates the degree of matching based on key knowledge points. The fusion weighting coefficient ranges from 0.3 to 0.7 and is dynamically adjusted based on the user session context.

[0014] Preferably, the system further includes a feedback learning mechanism configured to collect user interaction data on product pairing schemes; based on the interaction data, the parameters in the product matching model are updated using a gradient descent algorithm, including weight coefficients and threshold parameters, to continuously optimize the pairing effect; the update process is achieved by minimizing a loss function, which is defined as the difference between user feedback and model prediction, and the parameters are adjusted using an iterative optimization method.

[0015] Preferably, the system is implemented using a microservice architecture, with each module deployed as an independent service and communicating via a RESTful API. The system supports multi-dimensional analysis of knowledge points and real-time matching of products, with the total processing time for a single user request controlled within 2 seconds, and the system can be scaled up to handle more than 1,000 concurrent requests per second.

[0016] The technical effects and advantages of this invention are as follows: Compared to existing technologies, this invention achieves fully automated processing from user natural language questioning to product recommendation generation by constructing a collaborative working system comprising a question-and-answer interface module, an encyclopedia information analysis module, a product pairing module, and an output module. The system adopts a modular design, with each module exchanging data through standardized interfaces, forming an organic whole between knowledge acquisition, information analysis, and product recommendation. This system architecture avoids the cumbersome operation of switching between multiple independent platforms required in traditional solutions, significantly improving user experience and operational efficiency, while also providing a solid technical foundation for future functional expansion.

[0017] Compared to existing technologies, this invention introduces a semantic relevance calculation method based on a vector space model, employing a cosine similarity formula to accurately match knowledge points with query intent. This method transforms entity relationships and user query intent in the knowledge graph into high-dimensional vector representations, evaluating semantic relevance by calculating the cosine of the angle between vectors. This deep semantic-based matching mechanism effectively overcomes the limitations of traditional keyword matching, improves the accuracy and completeness of knowledge point extraction, and provides a more reliable knowledge foundation for subsequent product recommendations.

[0018] Compared to existing technologies, this invention designs a multi-dimensional evaluation system that includes compatibility scores, complementarity scores, and personalized matching degrees, and uses a weighted fusion algorithm to calculate the final score of the product combination. This evaluation system comprehensively considers the technical matching degree, functional complementarity, and user preference characteristics between products, and achieves flexible configuration in different scenarios through adjustable weight coefficients. This multi-objective optimization method can generate more reasonable and personalized product combination schemes, improve the professionalism and practicality of recommendation results, and meet the diverse needs of users.

[0019] Compared to existing technologies, this invention continuously optimizes the system's recommendation performance by establishing a dynamic user profile model and a feedback learning mechanism. The system constructs a preference model by analyzing historical user interaction data and adjusts model parameters based on real-time feedback using a gradient descent algorithm. This adaptive learning capability allows the system to continuously evolve as user needs change, maintaining the timeliness and accuracy of recommendations while reducing manual maintenance costs. Attached Figure Description

[0020] Figure 1 This is a flowchart of the core modules of the system of the present invention.

[0021] Figure 2 This is a diagram illustrating the knowledge extraction and semantic matching process of this invention.

[0022] Figure 3 This is a diagram illustrating the product matching and evaluation system of the present invention.

[0023] Figure 4 This is a diagram illustrating the personalized recommendation and optimization mechanism of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Example 1 As attached Figures 1 to 4 The implementation details of the encyclopedia information analysis and product matching system based on question-and-answer are as follows: Implementation Step 1 (Receiving and Parsing User Issues) The system receives natural language questions input by users through a question-and-answer interface module. This module is deployed on the web server, uses the HTTP protocol to receive user requests, and supports text input in UTF-8 encoding format. After a user submits a question, the system first performs text preprocessing operations, including: converting full-width characters to half-width characters, filtering special characters, merging consecutive spaces, and validating text length. The preprocessed text then enters the natural language processing flow.

[0026] The system employs a BERT-based sequence labeling model for word segmentation and part-of-speech tagging. This model is pre-trained on a corpus of millions of Chinese characters and contains a 12-layer Transformer structure with 768 hidden layers. During word segmentation, the model outputs a BIO-labeled sequence for each character to identify word boundaries. Part-of-speech tagging uses a set of 45 standard labels, including basic parts of speech such as nouns, verbs, and adjectives, as well as domain-specific parts of speech.

[0027] After completing the basic analysis, the system parses the question intent using an intent classification model. This model employs a multilayer perceptron structure, with sentence vector representations of text as input, and the output layer uses the Softmax function to calculate the probability distribution of each intent category. The model defines a total of 128 intent categories, covering scenarios such as product consultation, usage suggestions, and ingredient queries. During inference, the system selects intent categories with a probability value exceeding 0.8 as primary intents, while simultaneously recording all auxiliary intents with a probability exceeding 0.3.

[0028] The system generates a problem intent vector. The process is as follows: The preprocessed text is input into the BERT model, and the 768-dimensional vector corresponding to the [CLS] label of the last Transformer layer is taken as the text representation. The dimensionality is reduced to 256 dimensions through a fully connected layer, and finally the question intent vector is obtained. This vector is used for subsequent semantic matching calculations.

[0029] Implementation Step 2 (Encyclopedia Data Retrieval and Knowledge Graph Construction) The encyclopedia information analysis module retrieves relevant information from multiple data sources based on parsed structured queries. The system-maintained data source connection pool includes the following types: knowledge graph databases based on SPARQL endpoints, third-party knowledge services providing RESTful APIs, and directly connected MySQL relational databases. Each data source is configured with independent timeout parameters and retry strategies.

[0030] The data retrieval process employs multi-threaded concurrent execution. The main thread first breaks down the query into multiple subqueries, each optimized for a specific data source. The worker thread pool contains 10 threads, each responsible for querying one data source. The overall system timeout is set to 5 seconds, and the timeout for a single data source is 3 seconds.

[0031] After obtaining the raw data, the system performs a data cleaning process: removing HTML tags and JavaScript code to extract plain text content; filtering advertising text and navigation information; standardizing character encoding to UTF-8; standardizing date and time formats; and removing duplicate content. The cleaned text then enters the entity recognition process.

[0032] Entity recognition employs a BERT-based sequence labeling model, fine-tuned on domain-specific corpora, capable of recognizing 32 entity types, including product names, ingredients, efficacy, and skin types. For identified entities, the system extracts semantic relationships between them using a relation extraction model. This model utilizes a joint extraction architecture based on pointer networks, directly outputting triples of (head entity, relation type, tail entity).

[0033] Based on the extracted entities and relationships, the system constructs a local knowledge graph. The graph uses an attribute graph model, with nodes containing the following attributes: entity ID, entity type, entity name, confidence level, and data source. Edges contain the following attributes: relationship type, confidence level, and extraction model version. Graph data is stored in memory in JSON-LD format for subsequent processing.

[0034] Implementation Step 3 (Key Knowledge Point Extraction and Semantic Evaluation) After constructing the knowledge graph, the system executes a key knowledge point extraction process. First, each entity and relation in the graph is converted into a vector representation. (Entity vector) Generates as follows: Input entity description text into the Sentence-BERT model, and outputs a 384-dimensional sentence vector. Relation vector. It is obtained by looking up the relation type embedding matrix, which contains 256-dimensional vector representations of 64 predefined relation types.

[0035] For complex knowledge points (containing multiple entities and relationships), the system employs a graph neural network (GCN) for representation learning. A two-layer GCN model is used to encode the knowledge graph, aggregating neighbor node information to ultimately obtain the vector representation of the knowledge point. This vector has 256 dimensions and captures the structured semantic information of knowledge points.

[0036] Semantic relevance is calculated using the cosine similarity formula: in It is a knowledge point vector. This is the question intent vector. Calculation result. The value range is [-1, 1], and the closer the value is to 1, the stronger the semantic relevance.

[0037] System setting threshold ,when At that time, the knowledge point is marked as a candidate key knowledge point. For the candidate knowledge points that pass the initial screening, the system further calculates an importance score: in: , which represents the semantic relevance weight; , which is the source credibility weight; , which represents the weight of the timeliness factor; , where is the consistency coefficient weight; Based on the authority of the data source, it is divided into three levels: authoritative source is 1.0, ordinary source is 0.7, and user-generated content is 0.4. The calculation is based on the information update time: 1.0 for the last six months, 0.8 for the last year, and 0.5 for more than one year. This indicates the consistency of the knowledge point across different sources: 1.0 for complete consistency, 0.8 for basic consistency, and 0.2 for conflict.

[0038] The system selects importance scores. The top 20 knowledge points constitute a set of key knowledge points, and the score details for each knowledge point are recorded. This set serves as the core basis for product matching and is passed to the next module.

[0039] Implementation Step 4 (Multi-source Data Fusion and Knowledge Verification) The system performs data fusion on the set of key knowledge points. First, it performs clustering based on the cosine similarity of knowledge point vectors, using the DBSCAN algorithm with parameters set to: minimum sample size 2 and neighborhood radius 0.1. Semantically similar knowledge points are grouped together, and each group selects... The knowledge point with the highest score is used as the representative.

[0040] For the merged knowledge points, the system performs conflict detection. A conflict rule base is established, containing the following conflict patterns: Direct contradiction: A promotes B and A inhibits B; Conflict of degree: A slightly improves B versus A significantly improves B; Conflict of conditions: Under condition C, A promotes B, while under condition C, A inhibits B; When a conflict is detected, the system initiates a resolution mechanism: 1. Comparing knowledge points Values ​​should prioritize information from authoritative sources; 2. Comparison Values ​​should prioritize the most up-to-date information; 3. Comparison Values ​​should prioritize information consistent across multiple sources; 4. If all of the above are the same, keep both versions and mark the conflict status; After conflict resolution, the system performs logical consistency verification. Based on the described logic, a domain ontology is constructed, and the logical relationships between knowledge points are checked. Verification rules include: Category consistency: Instances must belong to a predefined category; Property consistency: The property values ​​of an instance must be within the range of their value range; Relational consistency: Relations must satisfy domain and range constraints; For any inconsistencies discovered during the verification process, the system records the exception type and the relevant knowledge points, adding corresponding explanatory comments to the output. Ultimately, a unified and consistent set of key knowledge points is generated for product matching.

[0041] Step 5 (Product Database Query and Feature Extraction) The product pairing module generates database query statements based on key knowledge points. The system converts each knowledge point into SQL query conditions, using the following mapping rules: Entity type knowledge points: Convert to IN statement in WHERE condition; Attribute knowledge point: Transform into equality judgment in WHERE conditions; Key relational concepts: Transform into join queries within JOIN conditions; The query execution adopts a phased strategy: the first phase uses core knowledge points for coarse screening and returns a maximum of 1,000 products; the second phase uses all knowledge points for fine screening and returns a maximum of 100 products; the third phase sorts based on matching degree and retains the top 50 products for subsequent processing.

[0042] For each product, the system extracts a feature vector. This vector contains the following components: Component feature vector (128 dimensions): Based on the product ingredient table, it is obtained by training the Word2Vec model; Efficacy feature vector (64 dimensions): Based on the product efficacy description, it is obtained by averaging word vectors weighted by TF-IDF; Price eigenvector (32-dimensional): includes price logarithm, price range normalized value, and price competitiveness index; User review feature vector (256 dimensions): extracted from user reviews using a sentiment analysis model; The feature vector generation process involves: first, standardizing each feature component to eliminate the influence of dimensions; then, using principal component analysis for dimensionality reduction to ensure dimensionality matching among the features; and finally, concatenating the dimensionality-reduced feature vectors into a complete product feature vector. The total dimensions are 480.

[0043] The system also records a matching log for each product against the query criteria, including: the matched knowledge point ID, the degree of matching, and the query response time. This log data is used for subsequent system optimization and performance evaluation.

[0044] Step 6 (Product Matching Calculation and Candidate Set Generation) The system calculates the matching degree based on product feature vectors and key knowledge point vectors. First, it calculates the matching degree between the product and a single knowledge point: in Indicates the first A vector of key knowledge points This represents the product feature vector. The calculation is performed in 480 dimensions of the vector space, and the computational efficiency is optimized using the BLAS library.

[0045] Then, by integrating all the key knowledge points, the product's knowledge matching degree is calculated: in Indicates the first The importance weight of each knowledge point is based on the importance score calculated in step 3 of the implementation process. The allocation is then performed. The specific calculation method is as follows: This ensures that the weights are normalized.

[0046] System settings matching threshold ,filter The products form an initial candidate set. For each product in the candidate set, the system queries the user profile module to obtain a user preference vector. This vector is obtained by analyzing users' historical behavior data, and its dimensions are the same as those of the product feature vector.

[0047] Calculate personalized matching degree: The final calculation of the product's overall matching degree: in This represents the weight of the knowledge matching degree. The system determines the weight based on... The candidate products are sorted by value, and the top 30 products are selected to enter the combination generation stage.

[0048] Step 7 (Product Combination Generation and Evaluation) The system generates combination pairings from the candidate product set. First, it constructs pairing templates based on product function categories, defining the following dimensions: Functional complementarity: basic nursing care + special nursing care + protective nursing care; Order of use: Cleansing products → Conditioning products → Moisturizing products → Protective products; Synergistic effects of ingredients: absorption-enhancing ingredients + active ingredients + stabilizing ingredients; For each template, the system uses a greedy algorithm to select products: starting with the product with the highest overall matching degree, it sequentially selects products that meet the template requirements and are compatible with other selected products. Each combination contains 3-5 products to ensure the completeness of functionality and the rationality of use.

[0049] For each combination, the system calculates three evaluation metrics: 1. Compatibility Score The calculation is based on a product component interaction database, considering incompatibilities and synergistic effects between components. The score ranges from [0,1] and is normalized using the sigmoid function.

[0050] 2. Complementarity score Calculation based on product feature coverage. First, establish a feature requirement matrix, then calculate the combined feature coverage ratio, and finally calculate using the following formula: in This represents the score of product i on function j, and m is the total number of functions.

[0051] 3. Personalized matching degree : Calculate the geometric mean of the personalization matching degree of all products in the combination.

[0052] Final evaluation score calculation: Among them, the weighting coefficient , , The system is based on The values ​​are sorted by combination, and the top 5 combinations are selected as the final recommendation.

[0053] Implement step 8 (Personalized adjustments and context adaptation) The system optimizes recommendation results based on user profiles and contextual information. The user profile module maintains the following data: Basic attributes: age, gender, skin type, sensitivity level; Behavioral data: browsing history, purchase records, rating data, search keywords; Preference settings: explicitly stated preferences, content actively blocked; Personalized adjustment process: 1. Price Range Adjustment: Based on users' historical spending patterns, a spending power index is calculated, and the price range of recommended products is dynamically adjusted accordingly. The spending power index is calculated based on purchase records from the past 6 months.

[0054] 2. Brand Preference Weighting: A brand preference model is established, calculating preference scores based on users' historical interaction data with each brand. Products from brands with high preference receive an additional 0.1 point on top of the overall match score.

[0055] 3. Ingredient Preference Optimization: Maintain user ingredient blacklists and whitelists. For ingredients on the blacklist, directly filter related products; for ingredients on the whitelist, appropriately increase the recommendation weight of related products.

[0056] Context adaptation considers the following factors: Seasonal characteristics: Establish a season-product association matrix and adjust product recommendation weights according to the current season; Geographic location: Geographic location information is obtained based on the user's IP address, taking into account the impact of climate conditions on the demand for skincare products; Special scenarios: Detect special scenario keywords in user queries, such as "travel" and "wedding", and enable corresponding recommendation strategies; The adjusted recommendations are reordered to ensure they align with both long-term user preferences and real-time needs.

[0057] Implement step 9 (Result rendering and interactive presentation) The output module uses a React framework to build its user interface, employing a component-based design. Each recommendation result includes the following display components: 1. Product Card Components: Product image and name, key attribute tags, matching indicator, and pricing information.

[0058] 2. Product Relationship Diagram: Use D3.js to draw product relationship diagrams, display the complementary relationships between products, and highlight core product nodes.

[0059] 3. Detailed parameter panel: ingredient list and efficacy description, usage instructions and precautions, user review summary.

[0060] 4. Comparison Tools: Supports parallel comparison of multiple schemes, highlights differences, parameter comparison tables, interactive interface functions, floating display of detailed information, one-click addition to comparison lists, real-time filtering and sorting, and collection and sharing functions.

[0061] The system also provides a recommendation explanation, generated using templated text: "This combination is recommended because of [key knowledge points]. These products complement each other in terms of [functionality], and [component synergy explanation] makes them particularly suitable for use by [user characteristics] in [usage scenario]." Implement step 10 (Feedback collection and model optimization) The system deploys a multi-dimensional feedback collection mechanism: 1. Explicit feedback collection: 5-star rating system, usefulness poll (yes / no), and text box for specific improvement suggestions.

[0062] 2. Implicit Feedback Analysis: Clickstream data analysis, dwell time statistics, conversion rate tracking, scroll depth monitoring, and feedback data are transmitted in real time to the data analysis module via a Kafka message queue. The system performs model parameter updates weekly. 1. Knowledge point weight renew: ,in The loss function is calculated based on user feedback.

[0063] 2. Threshold parameter , Adjustment: Find the optimal threshold point based on the precision-recall curve.

[0064] 3. Weighting coefficients , , Optimization: Use grid search to find... The parameter combination whose value is most correlated with user satisfaction.

[0065] 4. User Preference Vector renew: ,in This is the attenuation factor.

[0066] The system verifies the effect of parameter adjustment through A / B testing, with 50% of the traffic allocated to the experimental group and the control group respectively. The evaluation period is 2 weeks. The system is only fully released when the new parameters are superior to the old parameters at a statistical significance level of p<0.05.

[0067] Implementation Step 11 (System Monitoring and Performance Maintenance) The system establishes a comprehensive monitoring indicator system: 1. Performance metrics: Interface response time (P95 < 500ms), system throughput (QPS > 100), number of concurrent users, and error rate (< 0.1%).

[0068] 2. Business metrics: Recommendation click-through rate, conversion rate, user satisfaction rating, recommendation diversity index, and monitoring data are collected through Prometheus and displayed in real-time dashboards using Grafana.

[0069] The system is configured with multi-level alarms: Warning level: Abnormal single indicator, automatic capacity expansion; Severe level: Multiple indicators are abnormal, triggering a downgrade strategy; Emergency level: System unavailable, switch to backup cluster; Performance optimization measures: 1. Caching strategy: Use Redis to cache hot data and intermediate results; 2. Database optimization: Create composite indexes and perform query optimization regularly; 3. Load balancing: Employs a round-robin + weighted allocation algorithm; 4. Asynchronous processing: Non-real-time tasks are executed asynchronously through message queues. The system generates a weekly performance analysis report, including resource usage, bottleneck analysis, and optimization suggestions. A full-link stress test is performed monthly to ensure that the system capacity meets the needs of business growth.

[0070] Finally, the following points should be noted: First, in the description of this application, it should be noted that, unless otherwise specified and limited, the terms "installation", "connection", and "linkage" should be interpreted broadly, and can be mechanical or electrical connections, or internal connections between two components, or direct connections. "Up", "down", "left", "right", etc. are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may change. Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A question-and-answer based encyclopedic information analysis and product matching system, characterized in that, The system includes a question-and-answer interface module, an encyclopedia information analysis module, a product matching module, and an output module; the question-and-answer interface module is configured to receive natural language questions input by users and parse the intent of the questions using natural language processing technology to generate structured queries; The encyclopedia information analysis module is configured to extract key knowledge points from multiple encyclopedia data sources based on the parsed question intent. The key knowledge points include entity attributes, entity relationships, and domain concepts. The product pairing module is configured to, based on the key knowledge points, calculate the matching degree between product feature vectors and knowledge point vectors, filter relevant products from the product database, and generate product combination schemes containing pairing logic; the output module is configured to present the product combination schemes to the user in a structured form; wherein, the system achieves end-to-end processing from user questions to product pairing schemes through the collaborative operation of each module, and the data flow in the processing process is continuous and seamless.

2. The encyclopedia information analysis and product matching system based on question-and-answer as described in claim 1, characterized in that, The knowledge point extraction process performed by the encyclopedia information analysis module includes: performing entity recognition and relation extraction on the original text obtained from multiple encyclopedia data sources to construct knowledge graph fragments; evaluating the importance of the knowledge graph fragments based on the question intent, and selecting key knowledge points whose relevance to the user intent exceeds a preset threshold. The importance evaluation is achieved by calculating the semantic relevance score between the knowledge points and the question intent. The semantic relevance score is calculated using cosine similarity in the vector space model, where both the knowledge points and the question intent are represented as high-dimensional vectors.

3. The encyclopedia information analysis and product matching system based on question-and-answer as described in claim 2, characterized in that, The semantic relevance score is calculated using the following formula: in, The knowledge point vector is obtained by embedding entities and relationships in a knowledge graph fragment; The question intent vector is obtained through semantic parsing of the user's question; when Value exceeds preset threshold ,in If the value ranges from 0.7 to 0.9, then the knowledge point is identified as a key knowledge point and passed to the product matching module.

4. The encyclopedia information analysis and product matching system based on question-and-answer as described in claim 1, characterized in that, The product matching process performed by the product matching module includes: mapping key knowledge points to query vectors in the product feature space; calculating the matching degree between the query vectors and each product feature vector in the product database, wherein the matching degree is calculated by vector dot product or cosine similarity; and filtering products whose matching degree exceeds a second preset threshold. The products form a candidate product set, among which The value ranges from 0.6 to 0.8, and the matching degree is calculated based on the alignment between product attributes and key knowledge points.

5. The encyclopedia information analysis and product matching system based on question-and-answer as described in claim 4, characterized in that, The product pairing module is further configured to: identify compatible product combinations in the candidate product set based on entity relationships and attribute constraints in key knowledge points; sort and optimize product combinations by evaluating compatibility scores and complementarity scores between products; the compatibility score is calculated based on the degree of matching of product technical specifications, the complementarity score is calculated based on the completeness of product function coverage, and both the compatibility score and the complementarity score are converted into values ​​in the range of 0 to 1 through normalization processing.

6. The encyclopedia information analysis and product matching system based on question-and-answer as described in claim 5, characterized in that, The final evaluation score for the product portfolio is calculated using the following formula: in, Indicates the compatibility score. Indicates complementarity score, Indicates based on user preferences and product features Personalized matching degree , and Let be the weight coefficient, and satisfy... , The value range is from 0.3 to 0.

5. The value range is from 0.3 to 0.

5. The value ranges from 0.2 to 0.4; the final evaluation score is used to prioritize the product mix and generate the best combination.

7. The encyclopedia information analysis and product matching system based on question-and-answer as described in claim 1, characterized in that, The system also includes a user profiling module, configured to dynamically build and update a user preference model; the user preference model is built by analyzing users' historical interaction data, including the user's preference weight for product attributes and interest in product types; When generating product combination schemes, the product matching module uses the user preference model as input parameters to adjust the priority and filtering conditions of product matching.

8. The encyclopedia information analysis and product matching system based on question-and-answer as described in claim 7, characterized in that, The user preference model influences the product matching process in the following ways: by performing a dot product operation on the user preference vector and the product feature vector to obtain a personalized matching score; and by weighting and fusing the personalized matching score with the matching degree based on key knowledge points to form the final product ranking. Weighted fusion is achieved through the following formula: in, This indicates the final match score. Indicates the personalized matching score. Indicates the degree of matching based on key knowledge points. The fusion weighting coefficient ranges from 0.3 to 0.7 and is dynamically adjusted based on the user session context.

9. A question-and-answer based encyclopedia information analysis and product matching system according to claim 1, characterized in that, The system also includes a feedback learning mechanism configured to collect user interaction data on product pairing schemes; based on the interaction data, the parameters in the product matching model, including weight coefficients and threshold parameters, are updated using a gradient descent algorithm to continuously optimize the pairing effect; the update process is achieved by minimizing a loss function, which is defined as the difference between user feedback and model prediction, and the parameters are adjusted using an iterative optimization method.

10. A question-and-answer based encyclopedia information analysis and product matching system according to claim 1, characterized in that, The system is implemented using a microservice architecture, with each module deployed as an independent service and communicating via a RESTful API. The system supports multi-dimensional analysis of knowledge points and real-time matching of products, with the total processing time for a single user request controlled within 2 seconds. The system can be scaled up to handle more than 1,000 concurrent requests per second.

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